Real-time Market Operations Research Articles

Market-Level Integrated Detection Against Cyber Attacks in Real-Time Market Operations By Self-Supervised Learning

Market-Level Integrated Detection Against Cyber Attacks in Real-Time Market Operations By Self-Supervised Learning

Cyber-Impact Analysis for ISO Revenue Adequacy Considering FDIA in Real-Time Market Operations

The consensus on the potential of market-targeting cyberattacks to cause catastrophic damage has driven recent research on electricity market cybersecurity analysis. This paper identifies two missing components in current literature. First, ISO revenue adequacy has not been analyzed under the context of cyberattacks. The false data injection attacks (FDIAs) could disturb the market settlement impacting revenue adequacy for ISOs. The lack of such analysis prevents ISOs from comprehensively assessing the financial consequences of market cyberattacks. Second, market attackers need to anticipate the market-clearing results to maximize their attack objectives. Thus, current literature focuses on formulating the attacker model and the market-clearing model as a bilevel problem. However, the coupling between the attack decision, the dispatch at ex-ante, and the price calculation at ex-post have not been explored. To fill those two research gaps, this paper first analytically explores the impact of FDIAs on real-time market operations on ISO revenue adequacy. Then, cyber-impact analysis is proposed to numerically analyze the revenue adequacy. The attacker model, ex-ante dispatch model, and ex-post incremental model are formulated as a trilevel problem to provide a reliable cyber-impact analysis on revenue adequacy. The proposed analysis and platform are demonstrated with the New-England 39-bus system.

Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part II: Scalability and Applications

This two-part paper proposes a data-driven, robust, and scalable sparse estimation framework of distribution factors (DFs). Part I proposes a novel Adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -Lasso estimator with theoretically guaranteed robustness. For solving this estimator, common algorithms are not scalable to large-scale systems since they often fail to converge under parallelism. This is addressed in Part II, where a novel Online Parallel Stochastic Coordinate Descent algorithm with theoretically guaranteed scalability is proposed. It can simultaneously update massive DF estimates by stochastic parallelism with fast convergence. It also works recursively under a multi-core setting and does not need step-size tuning, which is applicable for cloud-computing-based online parallel estimation. Convergence bounds of this algorithm are theoretically proven, and its speedup and maximum parallel core number are derived. These guide practical computational resource allocation for high efficiency. The benefits of the framework are showcased in real-time market operations under locational marginal pricing (LMP) and financial transmission right (FTR) mechanisms for congestion management. Thanks to its sparsity-promoting effect, robustness, and scalability, it allows for correctly and fast solving LMPs, congestion patterns, and FTR revenues against uncertainties. Test results on IEEE 300- and European 9241-bus systems via Alibaba Cloud validate the high scalability of this algorithm and benefits of the proposed framework to system applications.

Internal hydro- and wind portfolio optimisation in real-time market operations

In this paper aspects related to handling of intraday imbalances for hydro and wind power are addressed. The definition of imbalance cost is established and used to describe the potential benefits of shifting from plant-specific schedules to a common load requirement for wind and hydropower units in the same price area. The Nordpool intraday pay-as-bid market has been the basis for evaluation of imbalances, and some main characteristics for this market has been described. We consider how internal handling of complementary imbalances within the same river system with high inflow uncertainty and constrained reservoirs can reduce volatility in short-term marginal cost and risk compared to trading in the intraday market. We have also shown that the imbalance cost for a power producer with both wind and hydropower assets can be reduced by internal balancing in combination with sales and purchase in a pay-as-bid intraday market.

A Binary-Optimization-Based Coordinated Cyber-Physical Attack for Disrupting Electricity Market Operation

A reliable smart grid should possess the necessary security mechanism against any attack aiming at disrupting the electricity market operation. The operation is performed to allocate the final nodal price as per the states estimated in each node. The state estimator can be misdirected by changing the network configuration both at physical and/or information layer, leading to disruption in the market operation. As compared to the physical attack or cyber attack carried out independently, a coordinated cyber-physical attack has a larger impact on the market operation. This article proposes a coordinated cyber-physical attack to maximize the electricity market disruption in terms of deviation in the generation cost between the day-ahead market and real-time market operations. The identification of physical attack causing maximum unintended deviation in the electricity market operation has been formulated as a binary optimization problem. The second stage of the proposed attack involves injecting a state preserving attack vector into the communication channel. The false data injection hides the impact of the physical attack, thereby, refraining the control center from taking necessary actions. The impact of the attack has been quantified in terms of variation in the generation cost and nodal prices between the preattack and postattack scenarios for IEEE 9, 14, and 39-bus power systems.

Market-Level Defense Against FDIA and a New LMP-Disguising Attack Strategy in Real-Time Market Operations

Traditional cyberattack strategies on the electricity market only consider bypassing bad data detections. However, our analysis shows that experienced market operators can detect abnormal locational marginal prices (LMPs) under the traditional attack model during real-time (RT) operations, because such attack model ignores the characteristics of the LMP itself and leads to price spikes that can be an easy-to-detect signal of abnormality. A detection approach based on the concept of critical load level (CLL) is used to help operators identify risky periods when operators would be prone to overlooking abnormal LMPs. During safe periods, the abnormal LMPs are identified according to the operator's experience, while in risky CLL intervals, a N-x cyber contingency analysis is proposed to help independent system operators (ISOs) detect abnormal LMPs. Further, this paper constructs a new type of cyberattack strategy capable of not only bypassing bad data detection in the state estimation stage but also disguising the compromised LMPs as regular LMPs to avoid market operators’ alerts in a realistic scenario wherein the attacker has imperfect information on system topology. Finally, the proposed analysis method and the attack strategy are evaluated through numerical studies on the PJM 5-bus system and the IEEE 118-bus system.

Framework for large-scale implementation of wholesale-retail transactive control mechanism

Transactive energy is a control technique that uses market mechanisms to achieve desired control objectives. Several simulation studies and field demonstrations were carried out in recent years, but all focused on small scale systems and purpose-built simplified models which are not always capable of pointing out all the advantages and shortcomings of transactive energy methods. This work describes a co-simulation framework built on a hierarchical control architecture that allows for conducting studies on the impacts of a very large-scale deployment of transactive energy. The hierarchical transactive control architecture adopted in this work helps with alleviating computation and communication burden to facilitate a more effective large scale real-time market operation among device level resources and the system level operators. The co-simulation framework is evaluated using off-the-shelf simulators on an integrated power system model of unprecedented scale composed of the Western Electricity Coordination Council transmission system with tens of thousands of distribution systems deployed with flexible device-level distributed energy resources.

Integrity Data Attacks in Power Market Operations

We study the economic impact of a potential class of integrity cyber attacks, named false data injection attacks, on electric power market operations. In particular, we show that with the knowledge of the transmission system topology, attackers may circumvent the bad data detection algorithms equipped in today's state estimator. This, in turn, may be leveraged by attackers for consistent financial arbitrage such as virtual bidding at selected pairs of nodes. This paper is a first attempt to formalize the economic impact of malicious data attacks on real-time market operations. We show how an attack could systematically construct a profitable attacking strategy, in the meantime being undetected by the system operator. Such a result is also valuable for the system operators to examine the potential economic loss due to such cyber attack. The potential impact of the false data injection attacks is illustrated on real-time market operations of the IEEE 14-bus system.

Unified solution of a non-convex SCUC problem using combination of modified Branch-and-Bound method with Quadratic Programming

In this paper, a new practical method is presented for solving the non-convex security constraint unit commitment (SCUC) problem in power systems. The accuracy of the proposed method is desirable while the shorter computation time makes it useful for SCUC solution of large-scale power systems, real-time market operation and long-term SCUC problems. The proposed framework allows inclusion of the valve point effects, warmth-dependent start-up costs, ramp rates, minimum up/down time constraints, multiple fuels costs, emission costs, prohibited operating zones and AC power flow limits in normal and contingency conditions. To solve the non-convex problem, combination of a modified Branch-and-Bound method with the Quadratic Programming is used as an optimization tool and a developed AC power flow algorithm is applied for considering the security and contingency concerns using the nonlinear/linear AC model. These modifications improve the convergence speed and solution precision of SCUC problem. In the proposed method, in contrast with traditional SCUC algorithms, unit commitment solution, checking and satisfying the security constraints are managed simultaneously. The obtained results are compared with other reported methods for investigating the effectiveness of the proposed method. Also, the proposed method is applied to an Iranian power system including 493 thermal units.

Practices on real-time market operation evaluation

Based on multi-settlement market design, this paper introduces PJM's practices on real-time (RT) market operation evaluation, using uplift and a newly developed approach, perfect dispatch, as indicators to measure RT market operation efficiency. The evaluation views the balance between operation risk and economic efficiency in the integrated system and market operation. Some operational improvement and challenges identified through the evaluation are discussed along with potential solutions.